Modal Decomposition for Bearing Fault Detection

Bouchikhi, El Houssin El; Delpha, Claude; Benbouzid, Mohamed

doi:10.1002/9781119720317.ch4

Cited by 4 publications

(5 citation statements)

References 58 publications

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“…The feasibility of this method was proved through experimental simulation [ 24 ]. Amirat et al (2020) put forward a method based on variable modal decomposition combined with the optimized SVM network for joint FD [ 25 ]. Qu et al (2017) combined sparse expression technology and used its advantages in signal processing to extract features and identify faults of rolling machinery fault signals, achieving better diagnostic results [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Luo

Jiao

2021

PLoS ONE

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The purposes are to meet the individual needs of leather production, improve the efficiency of leather cutting, and increase the product’s competitiveness. According to the existing problems in current leather cutting systems, a Fault Diagnosis (FD) method combining Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) of Gray Wolf Optimizer (GWO) is proposed. This method first converts the original signal into a scale spectrogram and then selects the pre-trained CNN model, AlexNet, to extract the signal scale spectrogram’s features. Next, the Principal Component Analysis (PCA) reduces the obtained feature’s dimensionality. Finally, the normalized data are input into GWO’s SVM classifier to diagnose the bearing’s faults. Results demonstrate that the proposed model has higher cutting accuracy than the latest fault detection models. After model optimization, when c is 25 and g is 0.2, the model accuracy can reach 99.24%, an increase of 66.96% compared with traditional fault detection models. The research results can provide ideas and practical references for improving leather cutting enterprises’ process flow.

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Section: Related Workmentioning

confidence: 99%

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Luo

Jiao

2021

PLoS ONE

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“…So, the main issue that rises is how to extract this IMF. To answer this question, in [63][64][65], a mode decomposition-based notch filter was developed.…”

Section: Ensemble Emd Principlementioning

confidence: 99%

“…The statistical tool known as Pearson's correlation is used to measure the distance, and to give a weight to dependency between two temporal series x(n) and y(n) [67]. This dependency is weighted by a coefficient denoted by r(x, y) and defined by (2.58); a value of this coefficient close to −1 or 1 indicates that x(n) and y(n) are highly correlated positively or negatively, respectively, while a value around 0 indicates that there is no dependency between x(n) and y(n) [65].…”

Section: Statistical Distance Measurementmentioning

confidence: 99%

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The signal demodulation techniques

Benbouzid

2020

Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems

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Condition monitoring of electrical machines is a broad scientific area, the ultimate purpose of which is to ensure the safe, reliable and continuous operation of electrical machines. Hence, the task of fault detection is still an art, because induction machines are widely used in variable speed drives and in renewable energy conversion systems.A deep knowledge about all the phenomena involved during the occurrence of a failure constitutes an essential background for the development of any failure detection and diagnosis system. For the failure detection problem, it is important to know if a failure exists or not in the electric machine via the processing of available measurements. This chapter provides then an approach based on a electric machine current data collection and attempts to highlight the use of demodulation techniques for failure detection for stationary and nonstationary cases.

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“…In the past few decades, other decomposition techniques have been applied to vibration time series [ 24 , 25 ], among them, the empirical mode decomposition (EMD) [ 26 , 27 ], ensemble empirical mode decomposition (EEEMD) [ 28 , 29 , 30 ] or variational mode decomposition (VMD) [ 31 , 32 , 33 ]. Despite its shortcomings such as mode mixing and end effects [ 27 , 34 , 35 ], the EMD is still very popular.…”

Section: Introductionmentioning

confidence: 99%

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Mezni

Delpha

Braham

2022

Entropy

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Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

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Modal Decomposition for Bearing Fault Detection

Cited by 4 publications

References 58 publications

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

Automatic fault detection of sensors in leather cutting control system under GWO-SVM algorithm

The signal demodulation techniques

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

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